# [1]黄钢石,张亚非,陆建江,等.一种受限非负矩阵分解方法[J].东南大学学报(自然科学版),2004,34(2):189-193.[doi:10.3969/j.issn.1001-0505.2004.02.011] 　Huang Gangshi,Zhang Yafei,Lu Jianjiang,et al.Constrained factorization method for non-negative matrix[J].Journal of Southeast University (Natural Science Edition),2004,34(2):189-193.[doi:10.3969/j.issn.1001-0505.2004.02.011] 点击复制 一种受限非负矩阵分解方法() 分享到： var jiathis_config = { data_track_clickback: true };

34

2004年第2期

189-193

2004-03-20

## 文章信息/Info

Title:
Constrained factorization method for non-negative matrix

1 解放军理工大学通信工程学院, 南京 210007; 2 东南大学计算机科学与工程系, 南京 210096; 3 江苏省软件质量研究所, 南京 210096
Author(s):
1 Institute of Communication Engineering, PLA University of Science and Technology, Nanjing 210007, China
2 Department of Computer Science and Engineering, Southeast University, Nanjing 210096, China
3 Jiangsu Inst

Keywords:

TP18
DOI:
10.3969/j.issn.1001-0505.2004.02.011

Abstract:
A novel method, constrained non-negative matrix factorization, is presented to capture the latent semantic relations. The objective function of constrained non-negative matrix factorization is defined by imposing three additional constraints, in addition to the non-negativity constraint in the standard non-negative matrix factorization. The update rules to solve the objective function with these constraints are presented, and its convergence is proved. In contrast to the standard non-negative matrix factorization, the constrained non-negative matrix factorization can capture the semantic relations as orthogonal as possible. The experiments indicate that the constrained non-negative matrix factorization has better precision than the standard non-negative matrix factorization in information retrieval.

## 参考文献/References:

[1] Lee D D,Seung H S.Learning the parts of objects by non-negative matrix factorization [J].Nature,1999,401:788-791.
[2] Lee D D,Seung H S.Algorithms for non-negative matrix factorization [J].Advances in Neural Information Processing Systems, 2001,13:556-562.
[3] Tsuge S,Shishibori M,Kuroiwa S.Dimensionality reduction using non-negative matrix factorization for information retrieval [A].In:Proceedings of IEEE International Conference on Systems,Man,and Cybernetice[C].Tucson,USA,2001.960-965.
[4] Li S Z,Hou X W,Zhang H J.Learning spatially localized parts-based representation [A].In:Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition [C].Hawaii,USA,2001.207-212.
[5] Lu Jianjiang,Xu Baowen,Yang Hongji.Mining typical user profiles using non-negative matrix factorizationg [A].In:The Ninth International Conference on Distributed Multimedia Systems [C].Florida,USA,2003.105-109.
[6] Lu Jianjiang,Xu Baowen,Huang Gangshi,et al.Matrix dimensionality reduction for mining typical user profiles [J].Journal of Southeast University(English Edition),2003,19(3):231-235.

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